A Novel Approach to Voice of Customer Extraction using GPT-3.5 Turbo: Linking Advanced NLP and Lean Six Sigma 4.0

Abstract

Abstract This research breaks new ground by utilizing the advanced natural language processing (NLP) capabilities of OpenAI's GPT-3.5 Turbo model for the extraction of Voice of Customer (VoC) data from online customer support interactions on Twitter. Traditional methods of VoC extraction have typically fallen short in capturing the richness and nuance of customer sentiment. Contemporary Machine Learning (ML) approaches, while improved, still struggle to interpret the contextual subtleties of digital customer communications effectively. This study showcases the innovative deployment of GPT-3.5 Turbo, demonstrating its superior performance in extracting VoC through a deeper understanding of conversational context and a more intuitive, chat-based data processing. Furthermore, the large-scale, multilingual processing capabilities of this model offer a more comprehensive and inclusive analysis of VoC. The study ties these advancements to Lean Six Sigma 4.0, illustrating how the integration of GPT-3.5 Turbo's transformative capabilities can elevate the customer-centric approach of Lean Six Sigma in the era of Industry 4.0. This innovative exploration points to a significant evolution in VoC analysis, offering potential for more insightful, real-time data-driven customer service strategies and a more substantial foundation for decision-making in product development and process improvement. Future research is encouraged to validate these preliminary findings and to investigate ethical considerations associated with the use of such advanced NLP models.